A method for generating time points based on a statistical distribution. The results are stored in the `times` slot of the `utilities` object.
Arguments
- x
An object of class `utilities`.
- ...
Additional arguments for generating time points:
- n
A positive integer. Length of observation times.
- distribution
A character string specifying the distribution of the observation times. Default is `"expmixture"`. Available options are: - `"expmixture"`: A mixture of two exponential distributions. - `"uniform"`: A uniform distribution. - `"exponential"`: A single exponential distribution. - `"gamma"`: A gamma distribution.
- lambda1
Mean (1/rate) of the exponential distribution or the first exponential distribution in a mixture of exponential distributions. Default is `130`.
- lambda2
Mean (1/rate) of the second exponential distribution in a mixture of exponential distributions. Default is `6.5`.
- p1
Weight of the first exponential distribution in a mixture of exponential distributions. Default is `0.15`.
- p2
Weight of the second exponential distribution in a mixture of exponential distributions. Default is `0.85`.
- a
Shape parameter of a gamma distribution or lower limit of the uniform distribution. Default is `0`.
- b
Scale parameter of a gamma distribution or upper limit of the uniform distribution. Default is `1`.
Value
An updated `utilities` object with the generated observation times stored in the `times` slot.
References
Eyheramendy S, Elorrieta F, Palma W (2018). “An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves.” Monthly Notices of the Royal Astronomical Society, 481(4), 4311-4322. ISSN 0035-8711, doi:10.1093/mnras/sty2487 , https://academic.oup.com/mnras/article-pdf/481/4/4311/25906473/sty2487.pdf.
Examples
set.seed(12917)
o1=iAR::utilities()
o1<-gentime(o1, n=200, distribution = "expmixture", lambda1 = 130,
lambda2 = 6.5,p1 = 0.15, p2 = 0.85)
st=o1@times
mean(diff(st))
#> [1] 28.4244
o1=iAR::utilities()
o1<-gentime(o1, n=200, distribution = "expmixture", lambda1 = 15,
lambda2 = 2.5,p1 = 0.15, p2 = 0.85)
st=o1@times
mean(diff(st))
#> [1] 4.592272